scholarly journals Intercomparison of the Performance of Four Data Assimilation Schemes in a Limited‐Area Model on Forecasts of an Extreme Rainfall Event over the Uttarakhand in Himalayas

2021 ◽  
Author(s):  
Rakesh S ◽  
Govindan Kutty
Keyword(s):  
Data Assimilation ◽  
Extreme Rainfall ◽  
Rainfall Event ◽  
Limited Area ◽  
Extreme Rainfall Event ◽  
Limited Area Model ◽  
Area Model

27 Years of Regional Cooperation for Limited Area Modelling in Central Europe

2018 ◽  
Vol 99 (7) ◽  
pp. 1415-1432 ◽  
Author(s):  
Yong Wang ◽  
Martin Belluš ◽  
Andrea Ehrlich ◽  
Máté Mile ◽  
Neva Pristov ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Central Europe ◽  
Regional Cooperation ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Member States ◽  
Limited Area Model ◽  
Area Model ◽  
Common System

AbstractThis paper describes 27 years of scientific and operational achievement of Regional Cooperation for Limited Area Modelling in Central Europe (RC LACE), which is supported by the national (hydro-) meteorological services of Austria, Croatia, the Czech Republic, Hungary, Romania, Slovakia, and Slovenia. The principal objectives of RC LACE are to 1) develop and operate the state-of-the-art limited-area model and data assimilation system in the member states and 2) conduct joint scientific and technical research to improve the quality of the forecasts.In the last 27 years, RC LACE has contributed to the limited-area Aire Limitée Adaptation Dynamique Développement International (ALADIN) system in the areas of preprocessing of observations, data assimilation, model dynamics, physical parameterizations, mesoscale and convection-permitting ensemble forecasting, and verification. It has developed strong collaborations with numerical weather prediction (NWP) consortia ALADIN, the High Resolution Limited Area Model (HIRLAM) group, and the European Centre for Medium-Range Weather Forecasts (ECMWF). RC LACE member states exchange their national observations in real time and operate a common system that provides member states with the preprocessed observations for data assimilation and verification. RC LACE runs operationally a common mesoscale ensemble system, ALADIN–Limited Area Ensemble Forecasting (ALADIN-LAEF), over all of Europe for early warning of severe weather.RC LACE has established an extensive regional scientific and technical collaboration in the field of operational NWP for weather research, forecasting, and applications. Its 27 years of experience have demonstrated the value of regional cooperation among small- and medium-sized countries for success in the development of a modern forecasting system, knowledge transfer, and capacity building.

scholarly journals Sequential Open-Boundary Control by Data Assimilation in a Limited-Area Model

1995 ◽  
Vol 123 (9) ◽  
pp. 2899-2909 ◽  
Author(s):  
Jieping Zou ◽  
William W. Hsieh ◽  
I. M. Navon
Keyword(s):  
Data Assimilation ◽  
Boundary Control ◽  
Open Boundary ◽  
Limited Area ◽  
Limited Area Model ◽  
Area Model

Three-dimensional variational data assimilation for a limited area model : Part II: Observation handling and assimilation experiments

2001 ◽  
Vol 53 (4) ◽  
pp. 447-468 ◽  
Author(s):  
M. Lindskog ◽  
N. Gustafsson ◽  
B. Navascués ◽  
K.S. Mogensen ◽  
X.-Y. Huang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Limited Area ◽  
Limited Area Model ◽  
Area Model

scholarly journals Regionally Enhanced Global (REG) 4D-Var

2018 ◽  
Vol 146 (12) ◽  
pp. 4015-4038
Author(s):  
Michael A. Herrera ◽  
Istvan Szunyogh ◽  
Adam Brainard ◽  
David D. Kuhl ◽  
Karl Hoppel ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Computational Cost ◽  
Limited Area ◽  
Atmospheric Flow ◽  
Forecast Performance ◽  
Limited Area Model ◽  
Area Model

Abstract A regionally enhanced global (REG) data assimilation (DA) method is proposed. The technique blends high-resolution model information from a single or multiple limited-area model domains with global model and observational information to create a regionally enhanced analysis of the global atmospheric state. This single analysis provides initial conditions for both the global and limited-area model forecasts. The potential benefits of the approach for operational data assimilation are (i) reduced development cost, (ii) reduced overall computational cost, (iii) improved limited-area forecast performance from the use of global information about the atmospheric flow, and (iv) improved global forecast performance from the use of more accurate model information in the limited-area domains. The method is tested by an implementation on the U.S. Navy’s four-dimensional variational global data assimilation system and global and limited-area numerical weather prediction models. The results of the monthlong forecast experiments suggest that the REG DA approach has the potential to deliver the desired benefits.

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